{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "679a670a9bf0b7f6",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## 1 什么是Matplotlib\n",
    "- 是专门用于开发2D图表(包括3D图表)\n",
    "- 以渐进、交互式方式实现数据可视化\n",
    "## 2 为什么要学习Matplotlib\n",
    "- 能将数据进行可视化,更直观的呈现\n",
    "- 使数据更加客观、更具说服力\n",
    "## 3 实现一个简单的Matplotlib画图 — 以折线图为例\n",
    "### 3.1 pyplot模块\n",
    "matplotlib.pytplot包含了一系列类似于matlab的画图函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "df980e8423255020",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-20T03:17:34.103647300Z",
     "start_time": "2024-02-20T03:17:34.093208200Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f9314746523fc95",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "### 3.2 图形绘制流程\n",
    "- 1.创建画布 -- plt.figure()\n",
    "    ```python\n",
    "    plt.figure(figsize=(), dpi=)\n",
    "    figsize:指定图的长宽\n",
    "          dpi:图像的清晰度\n",
    "          返回fig对象\n",
    "    ```\n",
    "\n",
    "- 2.绘制图像 -- plt.plot(x, y)\n",
    "    ```\n",
    "    以折线图为例：plt.plot(x, y)\n",
    "    ```\n",
    "- 3.显示图像 -- plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "119f6ef8eda06050",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "### 3.3 折线图绘制与显示\n",
    "举例：展现上海一周的天气,比如从星期一到星期日的天气温度如下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e3467f5599aab2e1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-20T03:19:33.545227200Z",
     "start_time": "2024-02-20T03:19:33.465835100Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 1.创建画布\n",
    "plt.figure(figsize=(10, 10), dpi=100)\n",
    "\n",
    "# 2.绘制折线图\n",
    "plt.plot([1, 2, 3, 4, 5, 6 ,7], [17,17,18,15,11,11,13])\n",
    "\n",
    "# 3.显示图像\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb859dd97552cb9c",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## 4 认识Matplotlib图像结构\n",
    "![](../.images/matplotlib图像结构.jpeg)\n",
    "## 5 小结\n",
    "- 什么是matplotlib【了解】\n",
    "    - 是专门用于开发2D(3D)图表的包\n",
    "- 绘制图像流程【掌握】\n",
    "    - 1.创建画布 -- plt.figure(figsize=(20,8))\n",
    "    - 2.绘制图像 -- plt.plot(x, y)\n",
    "    - 3.显示图像 -- plt.show()"
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